Intelligent particle sensor boasts high accuracy at a low cost

Electronic cigarette woman vaping
Using a patented ASIC processor to filter out noise, the Piera-1 is suitable for true real-time, precise airborne particulate matter monitoring, including vaping aerosol.(ljubaphoto/iStock/Getty Images Plus)

An early-stage startup founded in 2018, Piera Systems has developed a breakthrough approach for more accurately detecting and measuring the quantity and size of particles that are suspended in air, called the Piera-1. That’s a big deal, since particulate matter (PM) consists of a mixture of airborne solid particles and liquid droplets that can be inhaled and has the potential to cause serious health problems.

First, a little background on why accuracy matters when it comes to particulate matter: PM measurements are designated by the diameter of the particles, in micrometers (microns). A PM diameter of 2.5 microns or smaller is considered most hazardous to human health. Further, the World Health Organization classifies airborne PM ranging in size from 0.1-10 microns as a Group 1 carcinogen and the biggest environmental risk to health.

The most accurate way to measure PM is what is called the gravimetric method, which determines the particulate pollution in “mass concentration,” namely, the mass of particulate matter in 1 cubic meter of air. But this approach is expensive, bulky, and is not cost feasible for real time PM monitoring. Although less accurate, because they more are cost-effective, and easier to implement, optical particle counters (or dust sensors) have become the de facto means for measuring air. Moreover, the small size of these sensors allow for easy integration into air purifiers and air conditioners.

Traditional optical particle counters use what’s called the Low Pulse Occupancy time (LPO), which is proportional to PM concentration, to measure PM Levels.  LPO works by converting the light scattered by the particles into a voltage signal and then doing an estimate of various particulate sizes.

The Piera-1 Intelligent Particle Sensing Module (IPS)  consists of a photon-counting, highly sensitive optoelectronic sensor and low-cost red laser to achieve high sample rate and detects particles <0.3 microns. Because the  technology directly counts pulses of different levels of photon energy, Chief Operating Officer Vin Ratford says that it is able to achieve accuracy levels akin to the more expensive reference instruments.

“Other sensors use some filtering followed by sampling with an A/D,” said Ratford. “LPO systems are at 1 Khz, which basically means they see a particle “cloud” and estimate the amount.  Their detection is ’tuned’ and calibrated for PM2.5. All other sizes are estimates,” said Ratford. “Our patented ASIC does the conversion with dedicated circuits to filter out noise and measure particle sizes into programmable bins. We sample at 1 Mhz, which means we can detect smaller particles and count each one. Basically, we can do what the expensive reference instruments do at a lower cost.”

The price of typical LMO sensors ranges between <$10 and ~$25. IPS sensors run $30 to $95, depending on how many bin sizes are reported.

Ratford said that machine learning/AI and algorithms will further assist in the classification of the PM, for example in the company’s vape and smoke detector. Called Canāree it can identify and discriminate between cigarette and vape smoke, calculate the PM’s mass concentration dissipation, and other characteristics.

“At this time, we are still doing the Learning part of ML,” said Ratford. “We are focused on specific particle sources like vape and smoke, which have distinct signatures based on particle size and count. The resulting histograms can be used to create ML models that can then classify the sources. Canāree generates the histograms and we are using MS Azure to run the algorithms and the ML models.”

The company’s SenseiAQ software controls the sensors and Canāree devices, either locally or on a cloud service (MS Azure).  The device currently runs locally on Mac/Windows devices with plans to support IoS/Android in the future. SenseAQ retrieves the data from sensors or Canāree every 0.5 seconds, with a programmable sample rate.  When connected, the device sends data to the cloud once a minute, which is also programmable.  

The target applications for the IPS sensor are in air purifiers, HVAC systems and air quality monitors.  The product is offered as Air Quality Monitoring as a Service through SenseiAQ. Customers can choose to either buy and manage the devices through their own cloud service or opt to have Piera manage the devices under a yearly lease model that includes cloud services, data storage, and sensor maintenance. An evaluation kit to get users started is available as well as a three-month trial package.

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